An advanced approach to flexible manufacturing is to move robotic manipulators (also referred to as industrial arms), using an AGV or mobile robot, between workstations. This integrated system is referred to as a mobile manipulator. Prior to industrial acceptance and standards development for mobile manipulators, users of these new systems will expect manufacturers to provide real performance data to guide their procurement and assure suitability for given application tasks. A test method that uses an artifact, called the Reconfigurable Mobile Manipulator Artifact (RMMA), is described in [Bostelman RV, Li-Baboud Y, Legowik S, Hong TH, Foufou S., "Mobile Manipulator Performance Measurement Data". 2017 Jun 27] and compared to an optical tracking system that was used as ground truth for the RMMA and mobile manipulator. Measurement data of an AGV, an onboard robot arm, and an optical tracking system were recorded and are described in the paper and are available for download using the link available in this record. The data needed to make these three measurements was collected during two tests; both tests have corresponding timestamps relative to global positioning system (GPS) time, where the computer clocks are synchronized using the Network Time Protocol. It is expected that the user of the information within the paper and the data files will have sufficient knowledge to implement mobile manipulation testing and evaluation.

Mobile manipulators, which are robotic systems integrating an automatic or autonomous mobile base with a manipulator, can potentially enhance automation in many industrial and unstructured environments. Namely, large-scale manufacturing processes, typical in the aerospace, energy, transportation, and conformal additive manufacturing fields, encompass a notable subset of potential future mobile manipulator use-cases. Utilizing autonomous mobility for manipulator re-positioning could allow for continuous, simultaneous arm and mobile base cooperation, which is referred to as i.e., continuous performance. Continuous mobile manipulator capabilities may hold particular benefit for large, curved, and complex workpieces. However, such flexibility can also introduce additional sources of performance uncertainty, preventing mobile manipulators from satisfying stringent pose repeatability and accuracy requirements. To identify and quantify this uncertainty, the Configurable Mobile Manipulator Apparatus was developed by the National Institute of Standards and Technology. Previous test implementations with the apparatus included non-continuous mobile manipulator performance, such as static and indexed performance, but continuous performance measurement had only been previously demonstrated in simulation and on proof-of-concept hardware. This dataset was obtained through the transfer of simulations and algorithms for continuous registration to an industrial mobile manipulator platform and through a subsequent 2^3 factorial designed experiment to compare the performance and robustness of two continuous localization methods: 1) A deterministic spiral search and 2) A stochastic Unscented Kalman Filter (UKF) search across two selected mobile base speeds and sides of the CMMA. Supplementary data obtained prior to the experiment, such as source code, calibration data, mobile base map and configuration data, coordinate system measurements, and robot/client to ground-truth system time synchronization is also included, along with the analysis source code and results files generated in conducting the performance evaluation.

Mobile manipulators, which are robotic systems integrating an automatic or autonomous mobile base with a manipulator, can potentially enhance automation in many industrial and unstructured environments. Namely, large-scale manufacturing processes, typical in the aerospace, energy, transportation, and conformal additive manufacturing fields, encompass a notable subset of potential future mobile manipulator use-cases. Utilizing autonomous mobility for manipulator re-positioning could allow for continuous, simultaneous arm and mobile base cooperation, which is referred to as i.e., continuous performance. Continuous mobile manipulator capabilities may hold particular benefit for large, curved, and complex workpieces. However, such flexibility can also introduce additional sources of performance uncertainty, preventing mobile manipulators from satisfying stringent pose repeatability and accuracy requirements. To identify and quantify this uncertainty, the Configurable Mobile Manipulator Apparatus was developed by the National Institute of Standards and Technology. Previous test implementations with the apparatus included non-continuous mobile manipulator performance, such as static and indexed performance, but continuous performance measurement had only been previously demonstrated in simulation and on proof-of-concept hardware. This dataset was obtained through the transfer of simulations and algorithms for continuous registration to an industrial mobile manipulator platform and through a subsequent 2^3 factorial designed experiment to compare the performance and robustness of two continuous localization methods: 1) A deterministic spiral search and 2) A stochastic Unscented Kalman Filter (UKF) search across two selected mobile base speeds and sides of the CMMA. Supplementary data obtained prior to the experiment, such as source code, calibration data, mobile base map and configuration data, coordinate system measurements, and robot/client to ground-truth system time synchronization is also included, along with the analysis source code and results files generated in conducting the performance evaluation.

The dataset contains both the robot's high-level tool center position (TCP) health data and controller-level components' information (i.e., joint positions, velocities, currents, temperatures, currents). The datasets can be used by users (e.g., software developers, data scientists) who work on robot health management (including accuracy) but have limited or no access to robots that can capture real data. The datasets can support the: - Development of robot health monitoring algorithms and tools - Research of technologies and tools to support robot monitoring, diagnostics, prognostics, and health management (collectively called PHM) - Validation and verification of the industrial PHM implementation. For example, the verification of a robot's TCP accuracy after the work cell has been reconfigured, or whenever a manufacturer wants to determine if the robot arm has experienced a degradation. For data collection, a trajectory is programmed for the Universal Robot (UR5) approaching and stopping at randomly-selected locations in its workspace. The robot moves along this preprogrammed trajectory during different conditions of temperature, payload, and speed. The TCP (x,y,z) of the robot are measured by a 7-D measurement system developed at NIST. Differences are calculated between the measured positions from the 7-D measurement system and the nominal positions calculated by the nominal robot kinematic parameters. The results are recorded within the dataset. Controller level sensing data are also collected from each joint (direct output from the controller of the UR5), to understand the influences of position degradation from temperature, payload, and speed. Controller-level data can be used for the root cause analysis of the robot performance degradation, by providing joint positions, velocities, currents, accelerations, torques, and temperatures. For example, the cold-start temperatures of the six joints were approximately 25 degrees Celsius. After two hours of operation, the joint temperatures increased to approximately 35 degrees Celsius. Control variables are listed in the header file in the data set (UR5TestResult_header.xlsx). If you'd like to comment on this data and/or offer recommendations on future datasets, please email guixiu.qiao@nist.gov.

• 로봇의 수송역량이 가능한 물리적 공간에서의 자율주행을 위한 학습용 인공지능 데이터 획득을 통해 배송용 로봇의 시각 확보 기반 마련 • 구축된 학습용 데이터를 활용하여 자율주행 로봇 비즈니스모델과 프로세스 정립에 활용되어 AI 데이터를 적절하게 관리하고 효율적인 사용이 가능한 기반을 제공할 수 있는 배송용 시각 영상 데이터 마련 • 택배와 같은 말단 배송 수요가 증가하는 가운데, 아파트나 빌딩 등 고밀도 집적 시설 내 배송 효율성을 높이고 증가하는 수요에 대응할 수 있는 자동 화물 운송 셔틀 운영 시스템 개발을 위한 주변 환경 데이터의 구축

실제와 가상환경에서 취득한 RGB-D이미지와 그에 대응하는 로봇관절각도, End-effector 포즈, 힘/토크, 촉각데이터로 구성 된 로봇 그리퍼로 200종 이상의 물체를 조작하는 대규모 물리 환경 로봇 조작 데이터

중소벤처기업진흥공단 연수사업의 일환으로 중기연수원에서 진행되는 스마트공장 관련 연수과정에서 사용되는 연수용 실습장비 관련입니다. 본 목록에서 아래에 해당하는 데이터를 확인해 주십시오. - 칼럼명: 순번, 장비명, 모델명 및 형식, 도입년도, 수량, 장소, 활용분야

2D 이미지 데이터 기반으로 개발한 주행가능영역 인식 모델과 3D LiDAR 데이터를 기반으로 개발한 동적 장애물 인식 모델을 활용한 배송로봇의 비도로 환경 자율주행을 위한 원천, 가공 및 학습 모델링 데이터

자율주행 로봇의 실내외 통합 주행을 위한 주행 데이터 구축날씨 (맑음, 흐림, 비)와 아침/낮/저녁 등의 다양한 환경에서 실내 및 실외에서 로봇이 주행할 때 보이는 여러 객체들의 종류들과 객체 위치들의 데이터를 제공하여 실내 및/혹은 실외공간, 통합 공간 기반의 다양한 연구를 할 수 있는 데이터 제공

- 상수도관로의 이상을 손상 없이 탐지하기 위해, 소프트 스킨의 생체모방 주행 로봇을 내부 투입하고, 각종 센서 및 장비를 통한 영상/음향 정보를 수집 후, AI 기반 빅데이터 분석 통해 이상유무 판단 및 예측 수행할 데이터 구축함 <데이터의 한계> 외부 음향데이터가 기존에는 상수도관 내의 이상부분에서의 음향의 차이가 있을 것으로 예측하고 수집하였으나 이상징후의 종류에 따른 차이가 크지 않았음